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101. Lecture 14. A) Convergence‏‎ (19:04, 13 November 2019)
102. Lecture 14. B) Law of Large Numbers‏‎ (19:06, 13 November 2019)
103. Lecture 14. E) Central Limit Theorem‏‎ (15:19, 14 November 2019)
104. Lecture 15. E) Multiple Parameters‏‎ (13:47, 19 November 2019)
105. Lecture 16. B) Example: Coin Tossing‏‎ (14:26, 20 November 2019)
106. Lecture 16. D) Conjugate Priors‏‎ (14:31, 20 November 2019)
107. Lecture 16. E) Normal Distribution‏‎ (14:35, 20 November 2019)
108. Lecture 16. F) "Counterexample"‏‎ (14:36, 20 November 2019)
109. Lecture 16. H) Theorem: Berstein von-Mises‏‎ (14:41, 20 November 2019)
110. Lecture 15. C) Example: Hypothesis Test‏‎ (16:04, 21 November 2019)
111. Lecture 17. A) Ordinary Least Squares‏‎ (19:18, 2 December 2019)
112. Lecture 17. D) Bootstrapping‏‎ (19:46, 2 December 2019)
113. Lecture 17. B) Normal Linear Model‏‎ (20:19, 3 December 2019)
114. Lecture 17. C) Asymptotic Properties of OLS‏‎ (20:20, 3 December 2019)
115. Lecture 15. A) Asymptotic Properties of ML Estimators‏‎ (20:22, 3 December 2019)
116. Lecture 18. A) Multicollinearity‏‎ (15:57, 4 December 2019)
117. Lecture 18. B) Partitioned Regression‏‎ (16:02, 4 December 2019)
118. Lecture 18. C) Gauss-Markov Theorem‏‎ (20:18, 4 December 2019)

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